Predicting the insulation performance of SF₆ substitute gases through gas molecular structures has been a popular topic worldwide. The difficulty is that the relationships between the molecular structure and the gas insulation strength, global warming potential and boiling temperature are not clear, and general linear methods cannot be used to effectively extract the key factors. Based on published molecular structure parameters, the grey correlation method is used to extract the factors that affect the gas dielectric strength, global warming potential and boiling temperature in a dynamic (non-linear) approach. Further, to predict the dielectric strength, global warming potential and boiling temperature of gases, a linear regression method and the factors with high correlations are used as independent variables. Through the Pareto optimal solution, the dielectric strength is set as the target, the global warming potential and boiling temperature are set as the constraints, and the ranges of the molecular structure parameters of the SF₆ substitute gas are obtained. This research study provides an important reference regarding the SF₆ substitute gas analysis and provides a research foundation for the design and synthesis of new environmentally friendly gases used in power equipment.

中文翻译：

---

基于Pareto优化的气体绝缘性能预测模型研究

预测 SF _{6的绝缘性能}通过气体分子结构替代气体一直是世界范围内的热门话题。难点在于分子结构与气体绝缘强度、全球变暖潜能值和沸腾温度之间的关系尚不明确，无法用一般的线性方法有效提取关键因素。基于公开的分子结构参数，灰色关联法用于以动态（非线性）方法提取影响气体介电强度、全球变暖潜能值和沸腾温度的因素。此外，为了预测气体的介电强度、全球变暖潜能值和沸腾温度，采用线性回归方法和具有高相关性的因素作为自变量。通过 Pareto 最优解，_得到6种替代气体。该研究为SF ₆代用气体分析提供了重要参考，为电力设备用新型环保气体的设计与合成提供了研究基础。